Systems and methods for using radio frequency signals and sensors to monitor environments (e.g., indoor environments) are disclosed herein. In one embodiment, a system for providing a wireless asymmetric network comprises a hub having one or more processing units and at least one antenna for transmitting and receiving radio frequency (RF) communications in the wireless asymmetric network and a plurality of sensor nodes each having a wireless device with a transmitter and a receiver to enable bi-directional RF communications with the hub in the wireless asymmetric network. The one or more processing units of the hub are configured to execute instructions to determine at least one of motion and occupancy within the wireless asymmetric network based on a power level of the RF communications.
Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A system for providing a wireless asymmetric network, comprising: a mobile robot having one or more processing units and at least one antenna for transmitting and receiving radio frequency (RF) communications in the wireless asymmetric network to function as a RF device; a plurality of sensor nodes each having a wireless device with a transmitter and a receiver to enable bi-directional RF communications with the mobile robot in the wireless asymmetric network, wherein the plurality of sensor nodes are configured temporarily with a mesh-based architecture for a time period sufficient for wireless communications that are necessary to determine localization of at least one sensor node of the plurality of sensor nodes within the wireless asymmetric network based on a power level of the RF communications between the at least one sensor node and the mobile robot that moves and measures the power level of the RF communications at different positions of a robot path.
Wireless communication networks and localization. This invention addresses the challenge of accurately determining the location of sensor nodes within a wireless asymmetric network. The system includes a mobile robot equipped with processing units and an antenna for RF communications. The robot acts as an RF device within the network. A group of sensor nodes are also part of the system, each having a wireless device capable of two-way RF communication with the mobile robot. These sensor nodes are temporarily configured in a mesh-based architecture. This configuration lasts for a duration sufficient to perform necessary wireless communications for localization. The localization of at least one sensor node is achieved by measuring the power level of RF communications between that sensor node and the mobile robot. The mobile robot moves along a defined path, measuring the RF power levels at various positions. By analyzing these power level measurements, the location of the sensor node within the wireless asymmetric network can be determined.
2. The system of claim 1 , wherein the localization of the at least one sensor node of the plurality of sensor nodes is based on time of flight information of the wireless asymmetric network.
A wireless asymmetric network system includes multiple sensor nodes that communicate with a central controller. The system determines the location of each sensor node using time-of-flight (ToF) measurements from wireless signals transmitted within the network. The ToF-based localization method involves measuring the time delay between signal transmission and reception to estimate distances between nodes and the controller. This approach enables precise positioning of sensor nodes in environments where traditional localization techniques may be unreliable. The system may also include additional features such as adaptive signal processing to compensate for environmental interference and dynamic network topology adjustments to maintain accurate localization under varying conditions. The use of ToF measurements provides a robust solution for tracking sensor node positions in applications requiring high spatial accuracy, such as industrial monitoring, asset tracking, or environmental sensing. The system may further incorporate error correction algorithms to refine localization estimates based on historical data and environmental factors. By leveraging asymmetric communication, where nodes transmit data to the controller but not necessarily vice versa, the system optimizes power efficiency and reduces complexity while maintaining reliable localization performance.
3. The system of claim 1 , wherein the localization of the at least one sensor node of the plurality of sensor nodes is determined via RF communication between the at least one sensor node and the robot at various robot positions as the robot moves along a path.
A system for determining the location of sensor nodes in an environment using a mobile robot. The system addresses the challenge of accurately localizing sensor nodes in dynamic or unstructured environments where traditional fixed-position localization methods may fail. The robot moves along a predefined or adaptive path while communicating with the sensor nodes via radio frequency (RF) signals. By analyzing the RF signal characteristics, such as signal strength, time of flight, or angle of arrival, at multiple robot positions, the system calculates the relative or absolute position of each sensor node. This approach leverages the robot's mobility to gather diverse data points, improving localization accuracy compared to static methods. The system may also incorporate additional sensors on the robot, such as cameras or LiDAR, to enhance localization precision. The method is particularly useful in applications like industrial automation, environmental monitoring, or search-and-rescue operations where sensor nodes are deployed in areas with limited infrastructure. The system dynamically updates node positions as the robot continues to move, ensuring real-time accuracy. This technique reduces reliance on pre-installed reference points and adapts to changing environments, making it suitable for both indoor and outdoor deployments.
4. The system of claim 1 , wherein the plurality of sensor nodes include image capturing devices having a different height than the robot, the image capturing devices to capture images to be combined with images captured by the robot for an image based mapping of an environment.
This invention relates to a robotic system for environmental mapping, addressing challenges in accurately mapping environments using mobile robots. The system includes a robot equipped with sensors and a plurality of sensor nodes deployed in the environment. The sensor nodes are positioned at different heights than the robot and include image capturing devices. These devices capture images from various perspectives, which are combined with images captured by the robot's own sensors to generate a comprehensive, image-based map of the environment. By integrating data from multiple vantage points, the system improves mapping accuracy and coverage, overcoming limitations of single-robot or single-height sensor deployments. The sensor nodes may be stationary or mobile, and their height differences relative to the robot enhance the system's ability to detect and map features at different elevations, such as obstacles, surfaces, or structural elements. The combined imagery provides a more detailed and reliable representation of the environment, supporting applications in navigation, surveillance, and autonomous operation.
5. The system of claim 1 , further comprising: a hub having one or more processing units and at least one antenna for transmitting and receiving radio frequency (RF) communications in the wireless asymmetric network.
A system for wireless asymmetric communication networks addresses the challenge of efficiently managing data transmission in networks where uplink and downlink traffic volumes differ significantly. The system includes a hub with one or more processing units and at least one antenna for transmitting and receiving radio frequency (RF) communications. The hub processes data for transmission and reception, optimizing communication efficiency by adapting to asymmetric traffic patterns. The system may also include multiple nodes, each with processing capabilities and antennas for bidirectional communication with the hub. These nodes relay data to and from the hub, forming a network that dynamically adjusts to varying data loads. The hub and nodes coordinate to prioritize high-traffic directions, reducing latency and improving overall network performance. The system may further incorporate error correction and signal modulation techniques to enhance reliability in asymmetric communication environments. By dynamically allocating resources based on traffic direction, the system ensures efficient use of bandwidth and minimizes delays in high-demand directions. This approach is particularly useful in applications where uplink or downlink traffic dominates, such as IoT deployments or remote monitoring systems.
6. The system of claim 5 , wherein the localization for the plurality of sensor nodes is based on RF communications from at least one of the plurality of sensor nodes, the robot, and the hub.
A wireless sensor network system includes multiple sensor nodes, a robot, and a hub, where the robot and hub are used to deploy and manage the sensor nodes. The system localizes the sensor nodes using radio frequency (RF) communications from at least one of the sensor nodes, the robot, or the hub. The localization process determines the positions of the sensor nodes within the network, enabling accurate data collection and coordination. The robot may assist in deploying the sensor nodes, while the hub serves as a central control point for communication and data aggregation. The RF-based localization ensures precise positioning, which is critical for applications requiring spatial awareness, such as environmental monitoring, industrial automation, or asset tracking. The system may also include features for dynamic reconfiguration, where the robot adjusts node positions or the hub optimizes network topology based on environmental changes or operational requirements. The use of RF signals for localization provides a scalable and reliable solution, reducing the need for manual positioning and improving overall network efficiency.
7. The system of claim 5 , wherein the hub is powered by a mains electrical source and the plurality of sensor nodes are each powered by a battery source to form the wireless asymmetric network.
This invention relates to a wireless asymmetric network system for environmental monitoring or industrial applications. The system addresses the challenge of powering distributed sensor nodes in remote or hard-to-access locations where continuous mains power is unavailable. The network includes a central hub connected to multiple battery-powered sensor nodes, forming an asymmetric power architecture where the hub relies on a stable mains electrical source while the sensor nodes operate independently on battery power. The hub serves as a central data collection and communication point, receiving wireless transmissions from the sensor nodes, which may include environmental sensors, industrial monitoring devices, or other remote data acquisition units. The asymmetric design ensures reliable operation of the hub while extending the battery life of the sensor nodes through optimized power management and low-energy communication protocols. The system may also include features such as data aggregation, remote configuration, and fault detection to enhance operational efficiency. This architecture is particularly useful in applications where sensor nodes are deployed in dispersed locations, such as agricultural fields, industrial facilities, or smart city infrastructure, where maintaining continuous power to all nodes is impractical.
8. The system of claim 1 , wherein the robot and the plurality of sensor nodes have optical emitters and detectors to determine proximity between the robot and the plurality of sensor nodes.
9. The system of claim 1 , wherein the plurality of sensor nodes are configured temporarily with the mesh-based architecture for a time period sufficient for wireless communications that are necessary for determining location of the at least one sensor node of the plurality of sensor nodes within the wireless asymmetric network based on the power level of the RF communications between the plurality of sensor nodes and the robot.
This invention relates to a wireless asymmetric network system for determining the location of sensor nodes using a robot. The system addresses the challenge of accurately locating sensor nodes in environments where traditional positioning methods are unreliable, such as in industrial or remote settings with obstructions or interference. The system includes a robot equipped with wireless communication capabilities and a plurality of sensor nodes distributed within the network. The sensor nodes are temporarily configured in a mesh-based architecture for a specific time period, enabling wireless communications necessary for location determination. During this period, the robot communicates with the sensor nodes via radio frequency (RF) signals, and the power levels of these RF communications are analyzed to estimate the relative positions of the sensor nodes. The mesh-based architecture allows for dynamic routing and redundancy, ensuring reliable data transmission despite potential signal disruptions. Once the location data is collected, the sensor nodes may revert to a different network configuration if needed. This approach improves positioning accuracy in asymmetric networks where traditional methods like GPS or fixed infrastructure are impractical. The system is particularly useful in applications requiring real-time tracking of mobile or static assets in challenging environments.
10. A robot, comprising: a memory for storing instructions; one or more processing units to execute instructions for monitoring a plurality of sensor nodes in a wireless asymmetric network; and radio frequency (RF) circuitry to transmit RF communications to and receive RF communications from the plurality of sensor nodes in the wireless asymmetric network to at least partially determine localization of at least one sensor node of the plurality of sensor nodes based on a power level of the RF communications between the at least one sensor node and the robot that functions as a RF device, moves and measures the power level of the RF communications at different positions of a robot path, wherein the plurality of sensor nodes are configured with a mesh-based architecture temporarily during localization for a time period sufficient for wireless communications necessary for determining location of at least one sensor node of the plurality of sensor nodes.
This invention relates to a robot system for localizing sensor nodes in a wireless asymmetric network. The problem addressed is the challenge of accurately determining the position of sensor nodes in such networks, where communication is often one-way or unbalanced, making traditional localization techniques difficult. The robot includes a memory and processing units that execute instructions to monitor multiple sensor nodes. It uses RF circuitry to transmit and receive RF signals from the sensor nodes, measuring the power level of these communications at different positions along the robot's path. By analyzing these power levels, the robot can estimate the location of at least one sensor node. The sensor nodes are temporarily configured in a mesh-based architecture during the localization process, enabling the necessary wireless communications for accurate positioning. This temporary mesh setup ensures efficient data exchange while minimizing network complexity. The robot's mobility allows it to gather data from multiple vantage points, improving localization accuracy. The system is particularly useful in environments where fixed infrastructure for localization is unavailable or impractical.
11. The robot of claim 10 , wherein localization of at least one sensor node of the plurality of sensor nodes is based on time of flight information of the wireless asymmetric network.
This invention relates to robotic systems equipped with sensor nodes for environmental monitoring and localization. The problem addressed is the need for accurate and reliable localization of sensor nodes in a wireless asymmetric network, where communication is not necessarily bidirectional or symmetric, to enable precise robot navigation and environmental mapping. The robot includes a plurality of sensor nodes distributed across its structure or environment. These sensor nodes communicate wirelessly, forming an asymmetric network where data transmission may not be reciprocal. The localization of at least one sensor node is determined using time-of-flight (ToF) information derived from wireless signals within this network. Time-of-flight measures the time taken for a signal to travel between nodes, allowing precise distance calculations and thus accurate positioning. The system leverages the asymmetric nature of the network, meaning that even if some nodes cannot receive signals from others, the robot can still estimate their positions based on the available ToF data. This approach improves localization accuracy in dynamic or partially obstructed environments, where traditional symmetric networks may fail. The robot may use this localization data for navigation, obstacle avoidance, or environmental mapping tasks. The invention enhances robotic autonomy by providing robust sensor node positioning in challenging wireless communication scenarios, ensuring reliable operation in diverse environments.
12. The robot of claim 10 , wherein localization of at least one sensor node of the plurality of sensor nodes is determined via RF communication between the at least one sensor node and the robot at various robot positions as the robot moves along a path.
This invention relates to robotic systems equipped with multiple sensor nodes for environmental monitoring or data collection. The problem addressed is accurately determining the physical location of sensor nodes in an environment, which is challenging due to factors like signal interference, node mobility, or unknown initial positions. The solution involves a robot that moves along a predefined path while communicating with the sensor nodes via radio frequency (RF) signals. By analyzing signal characteristics (e.g., signal strength, time of flight, or phase differences) at multiple robot positions, the system calculates the precise location of each sensor node. The robot may use onboard sensors (e.g., odometry, inertial measurement units) to track its own position during movement. The method improves localization accuracy by leveraging the robot's mobility to gather diverse RF signal data from different angles and distances. This approach is particularly useful in dynamic or GPS-denied environments where traditional localization techniques (e.g., GPS or fixed anchor nodes) are unreliable. The system may also incorporate signal processing algorithms to filter noise and compensate for environmental factors affecting RF propagation. The invention enhances applications like environmental monitoring, asset tracking, or industrial automation where precise sensor node localization is critical.
13. The robot of claim 10 , wherein the one or more processing units are configured to execute instructions to capture images of an indoor environment, to receive location information from the wireless asymmetric network, to receive images from the plurality of sensor nodes, and to determine robot location based on the captured images of the indoor environment, location information, and images received from the plurality of sensor nodes.
A robotic system is designed for precise indoor localization by integrating multiple data sources. The robot operates in environments where traditional GPS signals are unavailable, addressing the challenge of accurate positioning in indoor settings. The system includes one or more processing units that capture images of the indoor environment using onboard cameras. Additionally, the robot receives location information from a wireless asymmetric network, which may include signals from Wi-Fi access points or other indoor positioning systems. The robot also collects images from a plurality of sensor nodes distributed throughout the environment. These sensor nodes may include cameras or other imaging devices strategically placed to provide additional visual data. The processing units analyze the captured images, wireless location data, and sensor node images to determine the robot's precise location. By fusing these diverse data sources, the system improves localization accuracy compared to relying on a single input. The approach leverages visual landmarks, network-based positioning, and distributed sensor data to enhance robustness in dynamic indoor environments. This method is particularly useful for autonomous navigation in warehouses, smart buildings, or other complex indoor spaces where precise localization is critical.
14. The robot of claim 11 , wherein the power level comprises received signal strength indicator (RSSI) information including baseline values of RSSI for a baseline level to be compared with threshold values of RSSI for a threshold level to determine a motion condition or an occupancy condition.
This invention relates to a robot equipped with wireless communication capabilities, specifically focusing on using received signal strength indicator (RSSI) data to detect motion or occupancy conditions. The robot includes a wireless communication module that measures RSSI values, which are compared against baseline and threshold levels to determine environmental changes. The baseline RSSI values represent normal signal conditions, while threshold values define deviations that indicate motion or occupancy. When the measured RSSI deviates from the baseline by exceeding or falling below the threshold, the robot identifies a motion or occupancy event. This approach leverages wireless signal fluctuations caused by movement or presence of objects or individuals within the robot's communication range. The system may integrate with other sensors or algorithms to enhance accuracy, but the core innovation lies in using RSSI as a primary or supplementary method for detecting environmental changes. This technique is particularly useful in applications where traditional motion sensors are impractical or where wireless communication is already in use, such as in robotic navigation, security monitoring, or smart home automation. The invention improves upon prior art by providing a non-intrusive, cost-effective way to monitor motion and occupancy using existing wireless infrastructure.
15. The robot of claim 10 , wherein the plurality of sensor nodes are configured temporarily with the mesh-based architecture for a time period sufficient for wireless communications necessary for determining location of at least one sensor node of the plurality of sensor nodes.
A robot includes a plurality of sensor nodes configured to form a mesh-based wireless network architecture. The mesh network is temporarily established for a time period sufficient to enable wireless communications among the sensor nodes, allowing the robot to determine the location of at least one sensor node within the network. The sensor nodes may include various types of sensors, such as proximity, environmental, or motion sensors, and are distributed across the robot or its environment. The mesh-based architecture dynamically routes data between nodes, ensuring reliable communication even if some nodes are obstructed or fail. Once the location determination is complete, the mesh network may be disbanded, and the sensor nodes may revert to an alternative communication mode or enter a low-power state. This temporary mesh configuration reduces power consumption and interference while ensuring accurate localization of sensor nodes, which is critical for applications such as autonomous navigation, environmental monitoring, or collaborative robotics. The system may also include processing circuitry to analyze sensor data and adjust the robot's operations based on the determined locations.
16. A method for monitoring a wireless network architecture, comprising: transmitting, with radio frequency (RF) circuitry including at least one antenna of a robot that functions as a RF device, RF communications to a plurality of sensor nodes in the wireless network architecture; receiving, with the RF circuitry including at least one antenna of the robot, RF communications from the plurality of sensor nodes each having a wireless device with a transmitter and a receiver to enable bi-directional RF communications with the RF circuitry of the robot in the wireless network architecture; determining, with one or more processing units of at least one of the robot and sensor nodes, power level information for RF communications between the robot and the plurality of sensor nodes; and configuring the plurality of sensor nodes temporarily with a mesh-based architecture during localization for a time period sufficient for wireless communications necessary for determining location of at least one sensor node of the plurality of sensor nodes; and determining localization of at least one sensor node of the plurality of sensor nodes within the wireless network architecture based on at least one of power level information and time of flight information of the RF communications between the robot and the at least one sensor node, wherein the robot moves and measures the power level of the RF communications at different positions of a robot path.
A wireless network monitoring system uses a robot equipped with RF circuitry and antennas to communicate with multiple sensor nodes in a wireless network. The robot transmits and receives RF signals to and from the sensor nodes, which also have wireless transmitters and receivers for bidirectional communication. The system measures power levels of these RF communications using processing units in either the robot or the sensor nodes. During localization, the sensor nodes are temporarily configured in a mesh-based architecture to facilitate wireless communication for determining their positions. The robot moves along a path, measuring RF power levels at different positions to determine the location of at least one sensor node based on power level information or time-of-flight data from the RF signals. This approach enables dynamic monitoring and precise localization of sensor nodes within the wireless network.
17. The method of claim 16 , wherein the power level information comprises received signal strength indicator (RSSI) information including instantaneous values of RSSI to be compared with threshold values to determine a motion condition or an occupancy condition.
This invention relates to wireless communication systems, specifically methods for detecting motion or occupancy using received signal strength indicator (RSSI) data. The technology addresses the challenge of accurately determining motion or occupancy in environments where traditional sensors may be impractical or costly. The method involves analyzing instantaneous RSSI values from wireless signals, such as those from Wi-Fi or other radio frequency (RF) sources, to detect changes in signal strength that indicate movement or presence. By comparing these instantaneous RSSI values against predefined threshold values, the system can determine whether motion or occupancy is occurring. The method leverages existing wireless infrastructure, eliminating the need for additional hardware like motion sensors or cameras. This approach is particularly useful in applications like smart home automation, security systems, or energy management, where passive monitoring of occupancy or motion is desired. The system can differentiate between different conditions, such as a person moving within a space versus a static presence, by evaluating fluctuations in RSSI over time. The use of instantaneous RSSI values allows for real-time detection, making the method suitable for dynamic environments. The invention provides a cost-effective and scalable solution for motion and occupancy detection using existing wireless networks.
18. The method of claim 16 , further comprising: capturing image data with the robot while moving; capturing image data that includes the robot with at least one sensor of the plurality of sensor nodes; and determining a mapping of the robot within an indoor environment based on the image data of the robot and the image data of the at least one sensor.
This invention relates to robotic navigation and mapping within indoor environments. The problem addressed is the need for accurate localization and mapping of a robot in dynamic or unstructured indoor spaces where traditional navigation methods may fail. The solution involves a robot equipped with multiple sensor nodes that capture image data while the robot moves. At least one of these sensor nodes also captures image data that includes the robot itself. By analyzing both sets of image data, the system determines the robot's position and orientation within the environment, creating a real-time mapping of the indoor space. This approach improves navigation accuracy by using the robot's own presence in the captured images as a reference point, allowing for self-localization even in environments with limited or changing landmarks. The method leverages visual data from multiple perspectives to enhance spatial awareness, ensuring reliable operation in complex indoor settings. The system dynamically updates the mapping as new image data is captured, adapting to changes in the environment. This technique is particularly useful for autonomous robots operating in warehouses, offices, or other indoor spaces where precise navigation is critical.
19. The method of claim 18 , further comprising: capturing multiple images that each include the robot with at least one sensor of the plurality of sensor nodes; and determining a distance from the robot to the at least one sensor that captured the multiple images based on at least one of a speed of the robot while moving, time information of the captured multiple images, and distance traveled of the robot while within a field of view of the at least one sensor.
Technical Summary: This invention relates to robotic systems that use sensor networks for localization and navigation. The problem addressed is accurately determining a robot's position relative to multiple sensor nodes in an environment, particularly when the robot is moving. Traditional methods may rely on static sensor data or require complex computations, leading to inaccuracies or computational overhead. The invention describes a method for improving robot localization by capturing multiple images of the robot using at least one sensor node. The robot moves within the sensor's field of view while the sensor records images. The system then calculates the robot's distance from the sensor based on the robot's movement speed, the timestamps of the captured images, and the distance the robot travels while visible to the sensor. This approach leverages motion dynamics to enhance positional accuracy without requiring additional hardware or extensive processing. The method ensures real-time tracking by continuously updating the robot's position as it moves, using temporal and spatial data from the sensor's observations. This is particularly useful in dynamic environments where static localization techniques may fail. The technique can be applied in autonomous navigation, industrial automation, or surveillance systems where precise robot positioning is critical. The invention improves upon prior art by integrating motion parameters with sensor data to achieve more reliable and efficient localization.
20. The method of claim 16 , further comprising: capturing image data with the robot while moving; capturing image data that includes the robot with two sensors of the plurality of sensor nodes; and determining localization of the two sensors based on the image data of the robot and the image data of the two sensors.
This invention relates to robotic systems that use distributed sensor networks for localization and navigation. The problem addressed is accurately determining the position of a robot within an environment where multiple sensor nodes are deployed, particularly when the robot is in motion. Traditional localization methods often struggle with dynamic environments or limited sensor coverage, leading to inaccuracies in robot positioning. The invention involves a method where a robot captures image data while moving through an environment. Simultaneously, two or more sensor nodes in the network also capture image data that includes the robot within their field of view. The system then processes this combined image data to determine the relative positions of the two sensors based on their observations of the robot. This approach leverages the robot's movement and the overlapping sensor coverage to improve localization accuracy. The method may also involve using the robot's captured images to refine sensor positions, ensuring that the sensor network remains calibrated over time. This technique enhances the robustness of robotic navigation in environments where sensor placement may be dynamic or partially occluded. The system can be applied in industrial automation, autonomous vehicles, or any scenario requiring precise robot localization within a sensor-equipped space.
Cooperative Patent Classification codes for this invention.
October 20, 2017
December 24, 2019
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